Imran Razzak
Biographic Data
| ID | 6312225 |
|---|---|
| NAME | Imran Razzak |
| GIVEN NAMES | Imran |
| FAMILY NAME | Razzak |
| SIGNATURE | RAZZAK I |
| AFFILIATIONS | UNSW Sydney |
| ORCID | 0000-0002-3930-6600 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
Performance Comparison of Transformer-Based Models on Twitter Health Mention Classification
Health mention classification classifies a given piece of text as a health mention or not. However, figurative usage of disease words makes the classification task challenging. To address this challenge, consideration of emojis and surrounding words of the disease names in the text can be helpful. Transformer-based methods are better at capturing the meaning of a word based on its surrounding words compared to traditional methods. However, there …
Identifying Rising Stars via Supervised Machine Learning
Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform pre…
Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey
Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos, may be interpreted to reflect the user’s activities, including their mental state regarding depression. For example, depression is a chronic disease…
Social responses to the Covid-19 pandemic
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Discovering dynamic adverse behavior of policyholders in the life insurance industry
Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery
Covidsenti: A Large-Scale Benchmark Twitter Data Set for Covid-19 Sentiment Analysis
Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes out…
Discovering dynamic adverse behavior of policyholders in the life insurance industry
Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery
Covidsenti: A Large-Scale Benchmark Twitter Data Set for Covid-19 Sentiment Analysis
Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes out…
Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects
Performance Comparison of Transformer-Based Models on Twitter Health Mention Classification
Health mention classification classifies a given piece of text as a health mention or not. However, figurative usage of disease words makes the classification task challenging. To address this challenge, consideration of emojis and surrounding words of the disease names in the text can be helpful. Transformer-based methods are better at capturing the meaning of a word based on its surrounding words compared to traditional methods. However, there …
Identifying Rising Stars via Supervised Machine Learning
Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform pre…
Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey
Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos, may be interpreted to reflect the user’s activities, including their mental state regarding depression. For example, depression is a chronic disease…
Social responses to the Covid-19 pandemic
Computer Science (7 works) · Artificial Intelligence (5 works) · Computer security (3 works) · Machine learning (3 works) · Sentiment Analysis and Opinion Mining (3 works) · Business (2 works) · Data science (2 works) · Engineering (2 works) · Geography (2 works) · Imbalanced Data Classification Techniques (2 works)